1. Base Content Slide Larry Ellison CEO, Oracle
"By having all of the pieces in the stack—from the silicon all the way up to the application—we'll be able to deliver systems that run faster, are fault-tolerant, are highly secure—much more secure, much more performance, much more cost-effective, much easier to use than we ever could have delivered by simply delivering components."
With our recent Sun acquisition, Oracle is uniquely positioned as the only vendor to provide a complete, integrated stack – from storage to scorecard. And as Larry Ellison states, by having this integrated stack, we are able to deliver systems that are faster, more secure, easier to use and more cost effective.

2. Extreme Performance Data Warehousing
Çetin Özbütün
Vice President, Data Warehousing Technologies

3. The Rise of the Intelligent Economy
“From recession comes an opportunity to reset a number of industry structures…there is an opportunity to infuse industries with technologies that position them to operate more effectively in the next 50 years.”
Lessons Learned in Building the Intelligent Economy, May 2010

4. All Businesses Want Better Insight
Industry
Typical Questions
Retail
What stores should be closed or sold?
Which customers will respond to new promotion?
Telecommunications
What are the issues effecting churn by region?
What is the average revenue per user (ARPU)?
Healthcare
What are most common patient service requests?
What is average level of clinical supplies on-hand?
Financial Services
How will new online services impact deposits?
How does average loan compare to last year?
Utilities
Who do we target for energy efficiency program?
What resources are needed to restore an outage?
Public Sector
What is the trend on budget and expenditures?
What is most cost-effective way to manage waste?

5. Challenge: Much More Data to Analyze
Data Warehouse Size and Growth
Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

6. Challenge: No Single Source of Truth Expensive Data Warehouse Architecture
Data Marts
OLAP
ETL
Data Mining
Data Marts
ETL
OLAP
Data Mining

7. Challenge: User Requirements Not Met
High Churn in Data Warehouse Platforms
Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

8. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

9. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

10. A Single Source of Truth?
Movie location see footnote
A Single Source of Truth?
Optional 2 minute video ‘A Single Source of Truth’ that explains benefit of data and server consolidation with Sun Oracle Database Machine.
WMV format video can be downloaded from here:
same library as PPT and video should play OK when clicked in slideshow mode.

11. Oracle Database 11g Oracle Exadata Database Machine
Consolidate Onto a Single Platform Faster Performance, Single Source of Truth
Data
Marts
Data Mining
Online
Analytics
ETL
Oracle Database 11g Oracle Exadata Database Machine

12. Oracle Exadata Database Machine For OLTP, Data Warehousing & Consolidated Workloads
Improve query performance by 10x
Better insight into customer requirements
Expand revenue opportunities
Consolidate OLTP and analytic workloads
Lower admin and maintenance costs
Reduce points of failure
Integrate analytics and data mining
Complex and predictive analytics
Lower risk
Streamline deployment
One support contact 12

13. Oracle Exadata Database Machine Family Oracle Exadata Database Machine X2-2
Oracle Database Server Grid
8 2-processor Database Servers
96 CPU Cores
768 GB Memory
Exadata Storage Server Grid
14 Storage Servers
5 TB Smart Flash Cache
336 TB Disk Storage
Unified Server/Storage Network
40 Gb/sec Infiniband Links
Available in full, half, quarter racks 13

14. Oracle Exadata Database Machine Family Oracle Exadata Database Machine X2-8
Oracle Database Server Grid
2 8-processor Database Servers
128 CPU Cores
2 TB Memory
Oracle Linux or Solaris 11 Express
Exadata Storage Server Grid
14 Storage Servers
5 TB Smart Flash Cache
336 TB Disk Storage
Unified Server/Storage Network
40 Gb/sec Infiniband Links 14

15. Traditional Query Problem
What Were Yesterday’s Sales?
Select sum(sales) where salesdate= ‘22-Jan-2010’…
Return entire Sales table
Discard most of sales table
Sum
Data is pushed to database server for processing
I/O rates are limited by speed and number of disk drives
Network bandwidth is strained, limiting performance and concurrency

16. Exadata Smart Scan Improve Query Performance by 10x or More
What Were Yesterday’s Sales?
Select sum(sales) where salesdate= ‘22-Jan-2010’…
Return Sales for Jan
Sum
Off-load data intensive processing to Exadata Storage Server
Exadata Storage Server only returns relevant rows and columns
Wide Infiniband connections eliminate network bottlenecks

17. Exadata Storage Index Transparent I/O Elimination with No OverheadB C D 1 3 5 8
Index
Min B = 1
Max B =5
Select * from Table where B< Only first set of rows can match
Min B = 3
Max B =8
Maintain summary information about table data in memory
Eliminate disk I/Os if MIN / MAX never match “where” clause
Completely automatic and transparent 17

18. Exadata Hybrid Columnar Compression Reduce Disk Space Requirements
Uncompressed Data
Data Warehouse Appliances
OLTP Data
DW Data
Archive Data
Oracle

19. Built-in Analytics Secure, Scalable Platform for Advanced Analytics
Oracle OLAP
Analyze and summarize
Oracle Data Mining
Uncover and predict
Complex and predictive analytics embedded into Oracle Database 11g
Reduce cost of additional hardware, management resources
Improve performance by eliminating data movement and duplication

20. Infrequently Used Data
Exadata Smart Flash Cache Extreme Performance for OLTP Applications
Frequently Used Data
Infrequently Used Data
Automatically caches frequently-accessed ‘hot’ data in flash storage
Assigns the rest to less expensive disk drives
Know when to avoid trying to cache data that will never be reused
Process data at 50GB/sec and up to 1million I/Os per second 20

21. With Partition Pruning
Benefits Multiply Converting Terabytes to Gigabytes
10 TB of User Data
1 TB of User Data
100 GB of User Data
10 TB of User Data
With 10x Compression
With Partition Pruning
SmartScan is very powerful, but just one of the ways the Database Machine ensures high I/O bandwidth.
For DW/BI applications, the many unique software capabilities of the Database Machine combine to vastly eliminate I/O.
Compression, pruning, storage indexes add aditional I/O elimination in addition to SmartScan – walk through this example.
Emphasize the “effective” outcome of scanning 10 TB of data in less than 1 second, through the combination of all of the techniques.
20 GB of User Data
5 GB of User Data
Sub second “10 TB” Scan
With Storage Indexes
10 TB of User Data
With Smart Scan
No Indexes

22. ETL with Oracle Fast data loading using DBFS and External Tables
BCP Unload
Staging Raw Files
Parallel Loads
FTP
Non-Oracle Source
Data Pump Unload
SCP
Oracle Source
Fast data loading using DBFS and External Tables
Fast transforms in Oracle Database 11g via Parallel DML operations
Best-in-class performance for large batch oriented data loads

23. Turkcell Runs 10x Faster on Exadata Compresses Data Warehouse by 10x
Replaced high-end SMP Server and 10 Storage Cabinets
Reduced Data Warehouse from 250TB to 27TB
Using OLTP & Hybrid Columnar Compression
Ready for future growth where data doubles every year
Experiencing 10x faster query performance
Delivering over 50,000 reports per month
Average report runs reduced from 27 to 2.5 mins
Up to 400x performance gain on some reports

24. Softbank Runs 2x–8x Faster on Exadata 36 Teradata Racks Replaced by 3 Exadata Racks
Exadata has more disk drives per rack, larger disk drives (2TB) and much better compression. This means that Exadata can hold much more User Data than other systems, and costs much less per user Terabyte.
Exadata User Data per SATA rack with 10x compression is 500TB.
Teradata has fallen very far behind in compression technology which makes them much more costly for large data environments.
Teradata 2580 holds 45 TB per cabinet using max sized 1TB drive and 1.3x compression (taken from Teradata specifications).
A single Exadata rack matches the user data capacity of the largest size Teradata (12s cabinet holding 517 TB user data).
The flagship Teradata 5600 is hold even less data per rack than the There is approximately a 20:1 Ratio of user data per rack comparing Exadatda to Teradata 5600.
Netezza Twinfin 32 TB Uncompressed per rack, 128 TB Compressed (assuming their maximum 4x compression).

25. Workload Management for DW Setting Up a Workload Management System
Define Workloads
Filter Exceptions
Manage Resources
Monitor Workloads
Adjust Plans
Execute Workloads
Monitor Workloads
Adjust Workload Plans
IORM
RAC
OEM
DBRM
Define Workload Plans
The RAC piece includes things like:
Services
Server Pools (Grid Infrastructure) to provide elasticity (add servers to pool to increase memory)
Instance Caging (consolidation)

26. Workload Management Request Queue Execute Assign Ad-hoc Workload
Each request:
Executes on a RAC Service
Which limits the physical resources
Allows scalability across racks
Assign
Each request assigned to a consumer group:
OS or DB Username
Application or Module
Action within Module
Administrative function
Ad-hoc Workload
Each consumer group has:
Resource Allocation (example: 10% of CPU/IO resources)
Directives (example: 20 active sessions)
Thresholds (example: no jobs longer than 2 min)
Reject
Downgrade

27. Workload Management Request Real-Time ETL Batch ETL Analytic Reports
Assign
Execute
Execute
OLTP Requests
Ad-hoc Workload
Queue
Downgrade
Reject

28. Workload Management Request Real-Time ETL Queue R-T 10% Batch ETL
Analytic
Reports
Analytic Reports 50%
Queue
Assign
OLTP Requests
OLTP 5%
Reject
Downgrade
Queue
Ad-hoc 25%
Ad-hoc Workload
Queue

29. Oracle Exadata for Data Warehousing
Movie location see
footnote
Oracle Exadata for Data Warehousing
Optional 3 minute video from customer BioWare that explains benefit of Oracle Exadata Database Machine for data warehousing in the online games industry.
WMV format video can be downloaded from here:
same library as PPT and video should play when cliicked when in slideshow mode.

30. Yaz Iida Chief Executive Officer
LinkShare
“Our continued investments in resources and technology, including the new Oracle Exadata database, is providing advertisers and publishers with the increased performance, usability, and innovation that will help drive strong revenue growth today and in the future.”
LinkShare press release, 9/28/2010

31. Vinod Haval Vice President and Manager, Database Paroducts
Bank of America
"We need one solution, one architecture. From that perspective, Exadata provides the right platform for consolidating database operations.”
Source: InfoWeek article 9/25/10

32. Oracle Exadata Momentum Rapid adoption in all geographies and industries

33. Oracle Database 11g The Best Database for Data Warehousing
Real Application Clusters
Advanced Compression
Partitioning
OLAP
Data Mining
World record performance for fast access to information
Manage growing volumes of information cost-effectively
Reduce costs through server and data consolidation

34. The Concept of Partitioning Maintain Consistent Performance as Database Grows
SALES
SALES
SALES
Europe
USA
Jan
Feb
Jan
Feb
Large Table
Difficult to Manage
Partition
Divide and Conquer
Easier to Manage
Improve Performance
Composite Partition
Higher Performance
Match to business needs

35. Partition for Performance Partition Pruning
Sales Table
5/19
What was the total sales amount for May 20 and May ?
Select sum(sales_amount)
From SALES
Where sales_date between
to_date(‘05/20/2010’,’MM/DD/YYYY’)
And
to_date(‘05/22/2010’,’MM/DD/YYYY’);
5/20
5/21
5/22
Performs operations only on relevant partitions
Dramatically reduces amount of data retrieved from disk
Improves query performance and optimizes resource utilization

36. Partition to Manage Data Growth Compress Data and Lower Storage Costs
Archive Data
Read Only Data
Active Data
15-50x Archive Compression
10-15x DW Compression
3x OLTP Compression
Distribute partitions across multiple compression tiers
Free up storage space and execute queries faster
No changes to existing applications

37. In-Memory Parallel Query in Database Tier
In-Memory Parallel Execution Efficient use of memory on clustered servers
In-Memory Parallel Query in Database Tier
Compress more data into available memory on cluster
Intelligent algorithm
Places table fragments in memory on different nodes
Reduces disk IO and speeds query execution
© 2010 Oracle Corporation

38. Automated Degree of Parallelism
Queue statements if not enough parallel servers available 64 32 16
When required number of servers are available, execute first statement
Automatically determine DOP 8
Execute immediately
Enough parallel servers available
Optimizer derives the best Degree of Parallelism
Based on resource requirements of all concurrent operations
Less DBA management, better resource utilization 38

39. Relational Star Schema
Summary Management Improve Response Time with Materialized Views
SQL Query
Region
Date
Sales by Date
Sales by Product
Sales by Region
Sales by Channel
Query Rewrite
Relational Star Schema
Products
Channel
Materialized Views
Pre-summarized information stored within Oracle Database 11g
Separate database object, transparent to queries
Supports sophisticated transparent query rewrite
Fast incremental refresh of changed data 39

40. Cube Organized Materialized Views
SQL Query
Summaries
Region
Date
Query Rewrite
Automatic Refresh
Products
Channel
Exposes Oracle OLAP cubes as relational materialized views
Provides SQL access to data stored in an OLAP cubes
Any BI tool or SQL application can leverage OLAP cubes

41. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

42. In-database Analytics Bring Algorithms to the Data, Not Data to the Algorithms
Analytic computations done in the database
Dimensional analysis
Statistical analysis
Data Mining
Scalability
Security
Backup & Recovery
Simplicity
OLAP
Statistics
Data Mining

43. Oracle OLAP Built-in Access to Analytic Calculations
How do sales in the Western region this quarter compare with sales a year ago?
What will sales next quarter be?
What factors can we alter to improve the sales forecast?
Multidimensional analytic engine that analyzes summary data
Offers improved query performance and fast, incremental updates
Embedded in Oracle Database instance and storage

44. Oracle OLAP and OBIEE Calculations Computed Faster in OLAP Engine

45. Oracle Data Mining Find Hidden Patterns, Make Predictions
Retail
Financial Services
Customer Segmentation
Response Modeling
Credit Scoring
Possibility of default
Communications
Utilities
Customer churn
Network intrusion
Product bundling
Predict power line failure
Healthcare
Public Sector
Patient outcome prediction
Fraud detection
Tax fraud
Crime analysis
Collection of data mining algorithms that solve business problems
Simplifies development of predictive BI applications
Embedded in Oracle Database instance and storage

46. Oracle Data Mining and OBIEE Prediction and Probability Results Integrated in Reports

47. Oracle Spatial and OBIEE
Enrich BI with map visualization of Oracle Spatial data
Enable location analysis in reporting, alerts and notifications
Use maps to guide data navigation, filtering and drill-down
Increase ROI from geospatial and non-spatial data

48. Oracle Exadata Intelligent Warehouse For Industries
Data Models
Business Intelligence
Exadata
Combine deep industry knowledge with data warehousing expertise
Help jump-start design and implementation of data warehouses
Available for Retail and Communications industries

49. Oracle Industry Data Models
Reference Data Model
Aggregate Data Model
Relational (STAR) for BI OLAP for Analytical
Derived Data Model
Data Mining/Complex Reports/Query
Base Data Model (3NF)
Atomic Level of Transaction Data
Combine deep industry knowledge with data warehousing expertise
Help jump-start design and implementation of data warehouses
Optimized for Oracle Database 11g and Oracle Exadata

50. Oracle Data Warehousing What Customers Think…
Movie location see footnote
Oracle Data Warehousing What Customers Think…
Optional 1 minute video montage of customers discussing the benefits of Oracle Database 11g (use instead of quote slides).
If you don’t want to use, see hidden customer quote slides that follow.
WMV format video can be downloaded from here:
Event Kit pages
=>
into same library as PPT and simply ‘insert movie from file’ to embed into this slide.

51. Henry Lovoy Data Manager
HealthSouth Corporation
“Oracle Database 11g, along with Oracle Real Application Clusters, Advanced Compression and Partitioning, all lend themselves to delivering highly available, high performance data warehousing.”
Source: 4/12/10 press release

52. Extreme Performance Data Warehousing Integrated Technology Stack
Smart Storage
Database
Data Models
ELT Tools
BI Tools
BI Applications
Single source of truth
Easy to deploy and manage
Extreme performance
Meets all end user requirements
Lower cost of ownership

53. Data Warehouse Reference Architecture

54. Data Warehouse Reference Architecture
Base data warehouse schema
Atomic-level data, 3nf design
Supports general end-user queries
Data feeds to all dependent systems
Application-specific performance structures
Summary data / materialized views
Dimensional view of data
Supports specific end-users, tools, and applications

55. Oracle #1 for Data Warehousing
Source: IDC, July 2009 – “Worldwide Data Warehouse Management Tools 2008 Vendor Shares”